Genetic Programming for Image Classification: An Automated Approach to Feature Learning (Adaptation, Learning, and Optimization)

Bi, Ying; Xue, Bing; Zhang, Mengjie

ISBN 10: 3030659291 ISBN 13: 9783030659295

Editorial: Springer, 2022

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This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate andpostgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.

Acerca del autor: Charlotte y Peter Fiell son dos autoridades en historia, teor�a y cr�tica del dise�o y han escrito m�s de sesenta libros sobre la materia, muchos de los cuales se han convertido en �xitos de ventas. Tambi�n han impartido conferencias y cursos como profesores invitados, han comisariado exposiciones y asesorado a fabricantes, museos, salas de subastas y grandes coleccionistas privados de todo el mundo. Los Fiell han escrito numerosos libros para TASCHEN, entre los que se incluyen 1000 Chairs, Dise�o del siglo XX, El dise�o industrial de la A a la Z, Scandinavian Design y Dise�o del siglo XXI.

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T�tulo: Genetic Programming for Image Classification...
Editorial: Springer
A�o de publicaci�n: 2022
Encuadernaci�n: Encuadernaci�n de tapa blanda
Condici�n: New

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Genetic Programming for Image Classification

Ying Bi|Bing Xue|Mengjie Zhang

Publicado por Springer International Publishing, 2022

ISBN 10: 3030659291 ISBN 13: 9783030659295

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Condici�n: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Introduces a series of typical Genetic Programming-based approaches to feature learning in image classificationProvides broad perceptive insights on what and how Genetic Programming can offer and shows a comprehensive and systematic research route. N� de ref. del art�culo: 564797287

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Genetic Programming for Image Classification | An Automated Approach to Feature Learning

Ying Bi (u. a.)

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ISBN 10: 3030659291 ISBN 13: 9783030659295

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Genetic Programming for Image Classification: An Automated Approach to Feature Learning (Adaptation, Learning, and Optimization)

Bi, Ying; Xue, Bing; Zhang, Mengjie

Publicado por Springer, 2022

ISBN 10: 3030659291 ISBN 13: 9783030659295

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Genetic Programming for Image Classification

Ying Bi

Publicado por Springer International Publishing, Springer Nature Switzerland Feb 2022, 2022

ISBN 10: 3030659291 ISBN 13: 9783030659295

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Taschenbuch. Condici�n: Neu. Neuware -This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate andpostgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 288 pp. Englisch. N� de ref. del art�culo: 9783030659295

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Genetic Programming for Image Classification : An Automated Approach to Feature Learning

Ying Bi

Publicado por Springer International Publishing, 2022

ISBN 10: 3030659291 ISBN 13: 9783030659295

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Taschenbuch. Condici�n: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate andpostgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation. N� de ref. del art�culo: 9783030659295

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Genetic Programming for Image Classification

Ying Bi

Publicado por Springer International Publishing Feb 2022, 2022

ISBN 10: 3030659291 ISBN 13: 9783030659295

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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania

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Taschenbuch. Condici�n: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation. 288 pp. Englisch. N� de ref. del art�culo: 9783030659295

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